Last updated: 2023-12-20

Checks: 5 2

Knit directory: ILD_ASE_Xenium/

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Rmd 83e2df3 heinin 2023-12-19 Adding lineage level annotations
html 83e2df3 heinin 2023-12-19 Adding lineage level annotations
Rmd b1f446f heinin 2023-12-15 Selecting PCs. Added utilities.

Introduction

Cell type annotations for spatial data.

Packages and environment variables

suppressPackageStartupMessages({library(cli)
                                library(Seurat)
                                library(SeuratObject)
                                library(SeuratDisk)
                                library(tidyverse)
                                library(tibble)
                                library(ggplot2)
                                library(ggpubr)
                                library(ggrepel)
                                library(workflowr)
                                library(googlesheets4)})
Loading Seurat v5 beta version 
To maintain compatibility with previous workflows, new Seurat objects will use the previous object structure by default
To use new Seurat v5 assays: Please run: options(Seurat.object.assay.version = 'v5')
setwd("/home/hnatri/ILD_ASE_Xenium/")
set.seed(9999)
options(ggrepel.max.overlaps = Inf)

# Colors, themes, cell type markers, and plot functions
source("/home/hnatri/ILD_ASE_Xenium/code/colors_themes.R")
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Epithelial''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Immune''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Mesenchymal''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Endothelial''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''All celltypes, annotated, merged''.
source("/home/hnatri/ILD_ASE_Xenium/code/plot_functions.R")
source("/home/hnatri/ILD_ASE_Xenium/code/utilities.R")

Import data

seurat_object <- readRDS("/tgen_labs/banovich/IPF/Spatial_ASE/ILD_ASE_Xenium_processed_npcs20.rds")
DefaultAssay(seurat_object)
[1] "RNA"

Feature expression

# Dimplot of clusters
DimPlot(seurat_object,
        group.by = "leiden_res0.5",
        cols = cluster_col,
        reduction = "umap",
        raster = T,
        label = T) +
  coord_fixed(ratio = 1) +
  NoLegend() +
  theme_minimal()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Version Author Date
83e2df3 heinin 2023-12-19 
# Cell type markers
DotPlot(seurat_object,
        features = unique(c(epithelial_features,
                            immune_features,
                            endothelial_features,
                            mesenchymal_features)),
        group.by = "leiden_res0.5",
        cols = c("azure", "tomato3")) +
  coord_flip() +
  theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17

Lineage annotations

epi_clusters <- c(9, 7, 8, 9)
imm_clusters <- c(3, 5, 6, 10)
endo_mesen_clusters <- c(1, 2, 4)

Epithelial

Subsetting clusters with any EPCAM expression and reclustering

epithelial <- subset(seurat_object, subset = leiden_res0.5 %in% epi_clusters)

# Saving the object for clustering with scanpy
#saveRDS(epithelial, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial.rds")
#epithelial <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_clustered.rds")

# Reclustering using Seurat. The function is sourced from /code/utilities.R
epithelial_reclustered <- recluster(epithelial)
# PCs for UMAP: 13
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
#saveRDS(epithelial_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_reclustered.rds")

Marker expression

#epithelial_reclustered <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_reclustered.rds")

# Dimplot of clusters
DimPlot(epithelial_reclustered,
        group.by = "snn_res.0.3",
        cols = cluster_col,
        reduction = "umap",
        raster = T,
        label = T) +
  coord_fixed(ratio = 1) +
  NoLegend() +
  theme_minimal()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

# Cell type markers
DotPlot(epithelial_reclustered,
        features = unique(c(epithelial_features,
                            immune_features,
                            endothelial_features,
                            mesenchymal_features)),
        group.by = "snn_res.0.3",
        cols = c("azure", "tomato3")) +
  coord_flip() +
  theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17

FeaturePlot(epithelial_reclustered,
            features = epithelial_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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FeaturePlot(epithelial_reclustered,
            features = immune_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 35 shown):
PPARG, LTB, HLA-DRA, CXCR4, PTPRC, CD69, CD3G, TRAC, ITM2C, CCL5
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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FeaturePlot(epithelial_reclustered,
            features = endothelial_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 23 shown):
APLN, HEY1, BMPR2, EPAS1, PECAM1, APLNR, COL15A1, POSTN, ZEB1, HAS1
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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FeaturePlot(epithelial_reclustered,
            features = mesenchymal_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 30 shown):
PI16, ELN, FAP, AXL, COL1A1, COL1A2, COL3A1, DCN, FN1, HAS2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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Some clusters are not epithelial:
* Cluster 3: endothelial
* Clusters 0, 4, 5, 6, 8, 9, 10: immune

Adding lineage to the metadata

epithelial_reclustered$lineage <- ifelse(epithelial_reclustered$snn_res.0.3 %in% c(1, 2), "Epithelial",
                                  ifelse(epithelial_reclustered$snn_res.0.3 %in% c(3), "Endothelial",
                                  ifelse(epithelial_reclustered$snn_res.0.3 %in% c(0, 4, 5, 6, 7, 8, 9, 10), "Immune", NA)))

#saveRDS(epithelial_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_reclustered.rds")

Immune

Subsetting clusters with immune marker expression and reclustering

immune <- subset(seurat_object, subset = leiden_res0.5 %in% imm_clusters)

# Saving the object for clustering with scanpy
#saveRDS(epithelial, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial.rds")
#epithelial <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_clustered.rds")

# Reclustering using Seurat. The function is sourced from /code/utilities.R
immune_reclustered <- recluster(immune)
# PCs for UMAP: 9
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
#saveRDS(immune_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/immune_reclustered.rds")

Marker expression

#immune_reclustered <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/immune_reclustered.rds")

# Dimplot of clusters
DimPlot(immune_reclustered,
        group.by = "snn_res.0.3",
        cols = cluster_col,
        reduction = "umap",
        raster = T,
        label = T) +
  coord_fixed(ratio = 1) +
  NoLegend() +
  theme_minimal()

# Cell type markers
DotPlot(immune_reclustered,
        features = unique(c(epithelial_features,
                            immune_features,
                            endothelial_features,
                            mesenchymal_features)),
        group.by = "snn_res.0.3",
        cols = c("azure", "tomato3")) +
  coord_flip() +
  theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17

FeaturePlot(immune_reclustered,
            features = epithelial_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17

FeaturePlot(immune_reclustered,
            features = immune_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 35 shown):
PPARG, LTB, HLA-DRA, CXCR4, PTPRC, CD69, CD3G, TRAC, ITM2C, CCL5

FeaturePlot(immune_reclustered,
            features = endothelial_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 23 shown):
APLN, HEY1, BMPR2, EPAS1, PECAM1, APLNR, COL15A1, POSTN, ZEB1, HAS1

FeaturePlot(immune_reclustered,
            features = mesenchymal_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 30 shown):
PI16, ELN, FAP, AXL, COL1A1, COL1A2, COL3A1, DCN, FN1, HAS2

Some clusters are not immune: * Cluster 5: epithelial * Clusters 3, 4: endothelial * Clusters

Adding lineage to the metadata

immune_reclustered$lineage <- ifelse(immune_reclustered$snn_res.0.3 %in% c(5), "Epithelial",
                              ifelse(immune_reclustered$snn_res.0.3 %in% c(3, 4), "Endothelial",
                              ifelse(immune_reclustered$snn_res.0.3 %in% c(0, 1, 2, 6, 7, 8), "Immune", NA)))

#saveRDS(immune_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/immune_reclustered.rds")

Endothelial and mesenchymal

Subsetting and reclustering

endo_mesen <- subset(seurat_object, subset = leiden_res0.5 %in% endo_mesen_clusters)

# Saving the object for clustering with scanpy
#saveRDS(epithelial, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial.rds")
#epithelial <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_clustered.rds")

# Reclustering using Seurat. The function is sourced from /code/utilities.R
endo_mesen_reclustered <- recluster(endo_mesen)
# PCs for UMAP: 10
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
#saveRDS(endo_mesen_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_reclustered.rds")

Marker expression

#endo_mesen_reclustered <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_reclustered.rds")

# Dimplot of clusters
DimPlot(endo_mesen_reclustered,
        group.by = "snn_res.0.3",
        cols = cluster_col,
        reduction = "umap",
        raster = T,
        label = T) +
  coord_fixed(ratio = 1) +
  NoLegend() +
  theme_minimal()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

# Cell type markers
DotPlot(endo_mesen_reclustered,
        features = unique(c(epithelial_features,
                            immune_features,
                            endothelial_features,
                            mesenchymal_features)),
        group.by = "snn_res.0.3",
        cols = c("azure", "tomato3")) +
  coord_flip() +
  theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17

FeaturePlot(endo_mesen_reclustered,
            features = epithelial_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
Rasterizing points since number of points exceeds 100,000.
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FeaturePlot(endo_mesen_reclustered,
            features = immune_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 35 shown):
PPARG, LTB, HLA-DRA, CXCR4, PTPRC, CD69, CD3G, TRAC, ITM2C, CCL5
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FeaturePlot(endo_mesen_reclustered,
            features = endothelial_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 23 shown):
APLN, HEY1, BMPR2, EPAS1, PECAM1, APLNR, COL15A1, POSTN, ZEB1, HAS1
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FeaturePlot(endo_mesen_reclustered,
            features = mesenchymal_features,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  NoLegend() &
  theme_minimal()
Warning: The following requested variables were not found (10 out of 30 shown):
PI16, ELN, FAP, AXL, COL1A1, COL1A2, COL3A1, DCN, FN1, HAS2
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Some clusters are not endothelial/mesenchymal:
* Cluster 0: epithelial
* Cluster 2, 4, 6, 7: immune

Adding lineage to the metadata

endo_mesen_reclustered$lineage <- ifelse(endo_mesen_reclustered$snn_res.0.3 %in% c(0), "Epithelial",
                                  ifelse(endo_mesen_reclustered$snn_res.0.3 %in% c(2, 4, 6, 7), "Immune", "Endo_Mesen"))

#saveRDS(endo_mesen_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_reclustered.rds")

Subsetting and merging at the lineage level

unique(epithelial_reclustered$lineage)
[1] "Immune"      "Endothelial" "Epithelial" 
unique(immune_reclustered$lineage)
[1] "Immune"      "Endothelial" "Epithelial" 
unique(endo_mesen_reclustered$lineage)
[1] "Immune"     "Endo_Mesen" "Epithelial"
epithelial_subsets <- list("epi1" = subset(epithelial_reclustered, subset = lineage == "Epithelial"),
                           "epi2" = subset(immune_reclustered, subset = lineage == "Epithelial"),
                           "epi3" = subset(endo_mesen_reclustered, subset = lineage == "Epithelial"))

immune_subsets <- list("imm1" = subset(epithelial_reclustered, subset = lineage == "Immune"),
                       "imm2" = subset(immune_reclustered, subset = lineage == "Immune"),
                       "imm3" = subset(endo_mesen_reclustered, subset = lineage == "Immune"))

endo_mesen_subsets <- list("em1" = subset(epithelial_reclustered, subset = lineage %in% c("Endo_Mesen", "Endothelial")),
                           "em2" = subset(immune_reclustered, subset = lineage %in% c("Endo_Mesen", "Endothelial")),
                           "em3" = subset(endo_mesen_reclustered, subset = lineage %in% c("Endo_Mesen", "Endothelial")))

epithelial_merged <- merge(x = epithelial_subsets[[1]],
                           y = epithelial_subsets[2:length(epithelial_subsets)])
immune_merged <- merge(x = immune_subsets[[1]],
                       y = immune_subsets[2:length(immune_subsets)])
endo_mesen_merged <- merge(x = endo_mesen_subsets[[1]],
                           y = endo_mesen_subsets[2:length(endo_mesen_subsets)])

# Saving
#saveRDS(epithelial_merged, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_merged.rds")
#saveRDS(immune_merged, "/scratch/hnatri/ILD/ILD_spatial_ASE/immune_merged.rds")
#saveRDS(endo_mesen_merged, "/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_merged.rds")

sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ComplexHeatmap_2.16.0   RColorBrewer_1.1-3      viridis_0.6.3          
 [4] viridisLite_0.4.2       ggthemes_5.0.0          googlesheets4_1.1.0    
 [7] workflowr_1.7.1         ggrepel_0.9.3           ggpubr_0.6.0           
[10] lubridate_1.9.2         forcats_1.0.0           stringr_1.5.0          
[13] dplyr_1.1.2             purrr_1.0.1             readr_2.1.4            
[16] tidyr_1.3.0             tibble_3.2.1            ggplot2_3.4.2          
[19] tidyverse_2.0.0         SeuratDisk_0.0.0.9021   Seurat_4.9.9.9048      
[22] SeuratObject_4.9.9.9084 sp_1.6-1                cli_3.6.1              

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.20       splines_4.3.0          later_1.3.1           
  [4] cellranger_1.1.0       polyclip_1.10-4        fastDummies_1.6.3     
  [7] lifecycle_1.0.3        rstatix_0.7.2          doParallel_1.0.17     
 [10] rprojroot_2.0.3        globals_0.16.2         processx_3.8.1        
 [13] lattice_0.21-8         hdf5r_1.3.8            MASS_7.3-60           
 [16] backports_1.4.1        magrittr_2.0.3         plotly_4.10.2         
 [19] sass_0.4.6             rmarkdown_2.22         jquerylib_0.1.4       
 [22] yaml_2.3.7             httpuv_1.6.11          sctransform_0.3.5     
 [25] spam_2.9-1             spatstat.sparse_3.0-1  reticulate_1.29       
 [28] cowplot_1.1.1          pbapply_1.7-0          abind_1.4-5           
 [31] Rtsne_0.16             BiocGenerics_0.46.0    git2r_0.32.0          
 [34] circlize_0.4.15        S4Vectors_0.38.1       IRanges_2.34.0        
 [37] irlba_2.3.5.1          listenv_0.9.0          spatstat.utils_3.0-3  
 [40] goftest_1.2-3          RSpectra_0.16-1        spatstat.random_3.1-5 
 [43] fitdistrplus_1.1-11    parallelly_1.36.0      leiden_0.4.3          
 [46] codetools_0.2-19       shape_1.4.6            tidyselect_1.2.0      
 [49] farver_2.1.1           stats4_4.3.0           matrixStats_1.0.0     
 [52] spatstat.explore_3.2-1 googledrive_2.1.0      jsonlite_1.8.5        
 [55] GetoptLong_1.0.5       ellipsis_0.3.2         progressr_0.13.0      
 [58] iterators_1.0.14       ggridges_0.5.4         survival_3.5-5        
 [61] foreach_1.5.2          tools_4.3.0            ica_1.0-3             
 [64] Rcpp_1.0.10            glue_1.6.2             gridExtra_2.3         
 [67] xfun_0.39              withr_2.5.0            fastmap_1.1.1         
 [70] fansi_1.0.4            callr_3.7.3            digest_0.6.31         
 [73] timechange_0.2.0       R6_2.5.1               mime_0.12             
 [76] colorspace_2.1-0       scattermore_1.1        tensor_1.5            
 [79] spatstat.data_3.0-1    utf8_1.2.3             generics_0.1.3        
 [82] data.table_1.14.8      httr_1.4.6             htmlwidgets_1.6.2     
 [85] whisker_0.4.1          uwot_0.1.14            pkgconfig_2.0.3       
 [88] gtable_0.3.3           lmtest_0.9-40          htmltools_0.5.5       
 [91] carData_3.0-5          dotCall64_1.0-2        clue_0.3-64           
 [94] scales_1.2.1           png_0.1-8              knitr_1.43            
 [97] rstudioapi_0.14        rjson_0.2.21           tzdb_0.4.0            
[100] reshape2_1.4.4         nlme_3.1-162           curl_5.0.0            
[103] GlobalOptions_0.1.2    cachem_1.0.8           zoo_1.8-12            
[106] KernSmooth_2.23-21     parallel_4.3.0         miniUI_0.1.1.1        
[109] pillar_1.9.0           vctrs_0.6.2            RANN_2.6.1            
[112] promises_1.2.0.1       car_3.1-2              xtable_1.8-4          
[115] cluster_2.1.4          evaluate_0.21          compiler_4.3.0        
[118] rlang_1.1.1            crayon_1.5.2           future.apply_1.11.0   
[121] ggsignif_0.6.4         labeling_0.4.2         ps_1.7.5              
[124] getPass_0.2-2          plyr_1.8.8             fs_1.6.2              
[127] stringi_1.7.12         deldir_1.0-9           munsell_0.5.0         
[130] lazyeval_0.2.2         spatstat.geom_3.2-1    Matrix_1.5-4.1        
[133] RcppHNSW_0.4.1         hms_1.1.3              patchwork_1.1.2       
[136] bit64_4.0.5            future_1.32.0          shiny_1.7.4           
[139] highr_0.10             ROCR_1.0-11            gargle_1.4.0          
[142] igraph_1.4.3           broom_1.0.4            bslib_0.4.2           
[145] bit_4.0.5